Automated bone marrow cell classification through dual attention gates dense neural networks.

Journal: Journal of cancer research and clinical oncology
PMID: 37740765

Abstract

PURPOSE: The morphology of bone marrow cells is essential in identifying malignant hematological disorders. The automatic classification model of bone marrow cell morphology based on convolutional neural networks shows considerable promise in terms of diagnostic efficiency and accuracy. However, due to the lack of acceptable accuracy in bone marrow cell classification algorithms, automatic classification of bone marrow cells is now infrequently used in clinical facilities. To address the issue of precision, in this paper, we propose a Dual Attention Gates DenseNet (DAGDNet) to construct a novel efficient, and high-precision bone marrow cell classification model for enhancing the classification model's performance even further.

Authors

  • Kaiyi Peng
    Department of Clinical Hematology, Key Laboratory of Laboratory Medical Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, No. 1, Yixueyuan Road, Chongqing, 400016, China.
  • Yuhang Peng
    Department of Clinical Hematology, Key Laboratory of Laboratory Medical Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, No. 1, Yixueyuan Road, Chongqing, 400016, China.
  • Hedong Liao
    Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Chongqing, 400016, China.
  • Zesong Yang
    Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Chongqing, 400016, China. yangzs@cqmu.edu.cn.
  • Wenli Feng
    Department of Clinical Hematology, Key Laboratory of Laboratory Medical Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine, Chongqing Medical University, No. 1, Yixueyuan Road, Chongqing, 400016, China. fengwl@cqmu.edu.cn.

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